Speckle size and decorrelation time; space–time correlation analysis of coherent light dynamically scattered from turbid media

2008 ◽  
Vol 281 (6) ◽  
pp. 1755-1760 ◽  
Author(s):  
Vinayakrishnan Rajan ◽  
Babu Varghese ◽  
Ton G. van Leeuwen ◽  
Wiendelt Steenbergen
Keyword(s):  
Correlation Analysis ◽  
Time Correlation ◽  
Space Time ◽  
Turbid Media ◽  
Coherent Light ◽  
Time Space ◽  
Speckle Size

Scanless nonlinear optical microscope for image reconstruction and space-time correlation analysis

2017 ◽  
Author(s):  
N. G. Ceffa ◽  
F. Radaelli ◽  
P. Pozzi ◽  
M. Collini ◽  
L. Sironi ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Correlation Analysis ◽  
Nonlinear Optical ◽  
Time Correlation ◽  
Optical Microscope ◽  
Space Time

Space–time correlation analysis of traffic flow on road network

2017 ◽  
Vol 31 (05) ◽  
pp. 1750027 ◽  
Author(s):  
Fei Su ◽  
Honghui Dong ◽  
Limin Jia ◽  
Zhao Tian ◽  
Xuan Sun
Keyword(s):  
Correlation Analysis ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Network ◽  
Road Traffic ◽  
Seasonal Pattern ◽  
Time Correlation ◽  
Space Time ◽  
Theoretical Assumption

Space–time correlation analysis has become a basic and critical work in the research on road traffic congestion. It plays an important role in improving traffic management quality. The aim of this research is to examine the space–time correlation of road networks to determine likely requirements for building a suitable space–time traffic model. In this paper, it is carried out using traffic flow data collected on Beijing’s road network. In the framework, the space–time autocorrelation function (ST-ACF) is introduced as global measure, and cross-correlation function (CCF) as local measure to reveal the change mechanism of space–time correlation. Through the use of both measures, the correlation is found to be dynamic and heterogeneous in space and time. The finding of seasonal pattern present in space–time correlation provides a theoretical assumption for traffic forecasting. Besides, combined with Simpson’s rule, the CCF is also applied to finding the critical sections in the road network, and the experiments prove that it is feasible in computability, rationality and practicality.

CASIMIR EFFECT FOR THE ROBIN SURFACES IN STATIC ROBERTSON–WALKER SPACE–TIME

2011 ◽  
Vol 20 (02) ◽  
pp. 161-168 ◽  
Author(s):  
MOHAMMAD R. SETARE ◽  
M. DEHGHANI
Keyword(s):  
Scalar Field ◽  
Energy Momentum Tensor ◽  
Casimir Effect ◽  
Vacuum Expectation ◽  
Robin Boundary Condition ◽  
Momentum Tensor ◽  
Space Time ◽  
Conformally Invariant ◽  
Time Space ◽  
Robin Boundary

We investigate the energy–momentum tensor for a massless conformally coupled scalar field in the region between two curved surfaces in k = -1 static Robertson–Walker space–time. We assume that the scalar field satisfies the Robin boundary condition on the surfaces. Robertson–Walker space–time space is conformally related to Rindler space; as a result we can obtain vacuum expectation values of the energy–momentum tensor for a conformally invariant field in Robertson–Walker space–time space from the corresponding Rindler counterpart by the conformal transformation.

scholarly journals Spatial and space-time correlations in systems of subpopulations with genetic drift and migration.

Genetics ◽  
1993 ◽  
Vol 133 (3) ◽  
pp. 711-727
Author(s):  
B K Epperson
Keyword(s):  
Population Genetics ◽  
Time Correlation ◽  
Space Time ◽  
Spatial Correlations ◽  
Gene Frequencies ◽  
Migration Patterns ◽  
Migration Rates ◽  
Space And Time ◽  
Time Correlations ◽  
And Migration

Abstract The geographic distribution of genetic variation is an important theoretical and experimental component of population genetics. Previous characterizations of genetic structure of populations have used measures of spatial variance and spatial correlations. Yet a full understanding of the causes and consequences of spatial structure requires complete characterization of the underlying space-time system. This paper examines important interactions between processes and spatial structure in systems of subpopulations with migration and drift, by analyzing correlations of gene frequencies over space and time. We develop methods for studying important features of the complete set of space-time correlations of gene frequencies for the first time in population genetics. These methods also provide a new alternative for studying the purely spatial correlations and the variance, for models with general spatial dimensionalities and migration patterns. These results are obtained by employing theorems, previously unused in population genetics, for space-time autoregressive (STAR) stochastic spatial time series. We include results on systems with subpopulation interactions that have time delay lags (temporal orders) greater than one. We use the space-time correlation structure to develop novel estimators for migration rates that are based on space-time data (samples collected over space and time) rather than on purely spatial data, for real systems. We examine the space-time and spatial correlations for some specific stepping stone migration models. One focus is on the effects of anisotropic migration rates. Partial space-time correlation coefficients can be used for identifying migration patterns. Using STAR models, the spatial, space-time, and partial space-time correlations together provide a framework with an unprecedented level of detail for characterizing, predicting and contrasting space-time theoretical distributions of gene frequencies, and for identifying features such as the pattern of migration and estimating migration rates in experimental studies of genetic variation over space and time.

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